Graph Deep Learning Lab

End-to-end training of neural network solvers for combinatorial problems such as the Travelling Salesman Problem is intractable and …

Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it …

Learning meaningful representations of free-hand sketches remains a challenging task given the signal sparsity and the high-level …

We propose a simple auto-encoder framework for molecule generation. The molecular graph is first encoded into a continuous latent …

We explore the impact of learning paradigms on training deep neural networks for the Travelling Salesman Problem. We design controlled …

This blog is based on the paper Benchmarking Graph Neural Networks which is a joint work with Chaitanya K. Joshi, Thomas Laurent, …

Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed …

The most famous NP-hard combinatorial problem today, the Travelling Salesman Problem, is intractable to solve optimally at large scale. …

In this talk, I will discuss a graph convolutional neural network architecture for the molecule generation task. The proposed approach …

In this talk, I will discuss how to apply graph convolutional neural networks to quantum chemistry and operational research. The same …